Logbook_Jasper
Jasper Jonker
2025-04-23
For our school project were are gone do an research like (Fibroblast-derived osteoglycin promotes epithelial cell repair)[https://pmc.ncbi.nlm.nih.gov/articles/PMC11937367/]. They are very specific and we are gone a look more global. I will look at the pathway analysis of the transcription.
For this i will use fgsea that is an tool that can be used for this kind of analysis.
First we are going to install it. That do we as follow:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("fgsea")if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("org.Mm.eg.db")After this we read the library in:
## ## ##############################################################################
## Pathview is an open source software package distributed under GNU General
## Public License version 3 (GPLv3). Details of GPLv3 is available at
## http://www.gnu.org/licenses/gpl-3.0.html. Particullary, users are required to
## formally cite the original Pathview paper (not just mention it) in publications
## or products. For details, do citation("pathview") within R.
##
## The pathview downloads and uses KEGG data. Non-academic uses may require a KEGG
## license agreement (details at http://www.kegg.jp/kegg/legal.html).
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## sibling.phylo tidytree## clusterProfiler v4.6.2 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
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## If you use clusterProfiler in published research, please cite:
## T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141##
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## select## After this i am going to use the example data to look how it all works.
oefen
data(examplePathways)
data(exampleRanks)fgseaRes <- fgsea(pathways = examplePathways,
stats = exampleRanks,
minSize = 15,
maxSize = 500)fgseaRes <- fgsea(pathways = examplePathways,
stats = exampleRanks,
eps = 0.0,
minSize = 15,
maxSize = 500)plotEnrichment(examplePathways[["5990980_Cell_Cycle"]],
exampleRanks) + labs(title="Programmed Cell Death")
topPathwaysUp <- fgseaRes[ES > 0][head(order(pval), n=1), pathway]
topPathwaysDown <- fgseaRes[ES < 0][head(order(pval), n=1), pathway]
topPathways <- c(topPathwaysUp, rev(topPathwaysDown))
plotGseaTable(examplePathways[topPathways], exampleRanks, fgseaRes,
gseaParam=0.5)
## Over-Representation Analysis
data("gcSample") # data for examples# Need to remove duplicates for the examples
all_genes <- unlist(gcSample)
universe <- all_genes[Biobase::isUnique(all_genes)] # all unique genes
# List with only unique genes
gcUnique <- lapply(gcSample, function(group_i) {
group_i[group_i %in% universe]
})# MSigDB R package)
msigdbr::msigdbr_collections() # available collections## # A tibble: 25 × 4
## gs_collection gs_subcollection gs_collection_name num_genesets
## <chr> <chr> <chr> <int>
## 1 C1 "" Positional 302
## 2 C2 "CGP" Chemical and Genetic Perturbati… 3494
## 3 C2 "CP" Canonical Pathways 19
## 4 C2 "CP:BIOCARTA" BioCarta Pathways 292
## 5 C2 "CP:KEGG_LEGACY" KEGG Legacy Pathways 186
## 6 C2 "CP:KEGG_MEDICUS" KEGG Medicus Pathways 658
## 7 C2 "CP:PID" PID Pathways 196
## 8 C2 "CP:REACTOME" Reactome Pathways 1736
## 9 C2 "CP:WIKIPATHWAYS" WikiPathways 830
## 10 C3 "MIR:MIRDB" miRDB 2377
## # ℹ 15 more rows# Subset to Human GO-BP sets
BP_db <- msigdbr(species = "Homo sapiens",
category = "C5", subcategory = "GO:BP")
head(BP_db)## # A tibble: 6 × 23
## gene_symbol ncbi_gene ensembl_gene db_gene_symbol db_ncbi_gene db_ensembl_gene
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 AASDHPPT 60496 ENSG0000014… AASDHPPT 60496 ENSG00000149313
## 2 ALDH1L1 10840 ENSG0000014… ALDH1L1 10840 ENSG00000144908
## 3 ALDH1L2 160428 ENSG0000013… ALDH1L2 160428 ENSG00000136010
## 4 MTHFD1 4522 ENSG0000010… MTHFD1 4522 ENSG00000100714
## 5 MTHFD1L 25902 ENSG0000012… MTHFD1L 25902 ENSG00000120254
## 6 BOLA2 552900 ENSG0000018… BOLA2 552900 ENSG00000183336
## # ℹ 17 more variables: source_gene <chr>, gs_id <chr>, gs_name <chr>,
## # gs_collection <chr>, gs_subcollection <chr>, gs_collection_name <chr>,
## # gs_description <chr>, gs_source_species <chr>, gs_pmid <chr>,
## # gs_geoid <chr>, gs_exact_source <chr>, gs_url <chr>, db_version <chr>,
## # db_target_species <chr>, entrez_gene <chr>, gs_cat <chr>, gs_subcat <chr># Convert to a list of gene sets
BP_conv <- unique(BP_db[, c("entrez_gene", "gs_exact_source")])
BP_list <- split(x = BP_conv$entrez_gene, f = BP_conv$gs_exact_source)
# First ~6 IDs of first 3 terms
lapply(head(BP_list, 3), head)## $`GO:0000002`
## [1] "10000" "131474" "1763" "9093" "2021" "3980"
##
## $`GO:0000012`
## [1] "200558" "54840" "2074" "1161" "3981" "142"
##
## $`GO:0000018`
## [1] "25" "60" "86" "10097" "126549" "200558"## Cluster GO-BP ORA with fgsea package
# For each cluster i, perform ORA
fgsea_ora <- lapply(seq_along(gcUnique), function(i) {
fora(pathways = BP_list,
genes = gcUnique[[i]], # genes in cluster i
universe = universe, # all genes
minSize = 15,
maxSize = 500) %>%
mutate(cluster = names(gcUnique)[i]) # add cluster column
}) %>%
data.table::rbindlist() %>% # combine tables
filter(padj < 0.05) %>%
arrange(cluster, padj) %>%
# Add additional columns from BP_db
left_join(distinct(BP_db, gs_subcat, gs_exact_source,
gs_name, gs_description),
by = c("pathway" = "gs_exact_source")) %>%
# Reformat descriptions
mutate(gs_name = sub("^GOBP_", "", gs_name),
gs_name = gsub("_", " ", gs_name))
# First 6 rows
head(fgsea_ora)## pathway pval padj overlap size
## 1: GO:0007267 2.256698e-05 0.037322954 40 233
## 2: GO:0045666 4.327299e-05 0.037322954 8 17
## 3: GO:0006754 8.172229e-06 0.009614126 12 24
## 4: GO:0015986 1.114681e-05 0.009614126 11 21
## 5: GO:0022900 7.265561e-05 0.025305396 14 37
## 6: GO:0042773 8.683660e-05 0.025305396 12 29
## overlapGenes cluster gs_subcat
## 1: 6833,477,652,775,1136,22849,... X3 GO:BP
## 2: 652,655,1746,2247,8200,1482,... X3 GO:BP
## 3: 4705,4695,4698,4700,4704,54539,... X7 GO:BP
## 4: 4705,4695,4698,4700,4704,54539,... X7 GO:BP
## 5: 1329,1537,4705,4695,4698,4700,... X7 GO:BP
## 6: 1329,1537,4705,4695,4698,4700,... X7 GO:BP
## gs_name
## 1: CELL CELL SIGNALING
## 2: POSITIVE REGULATION OF NEURON DIFFERENTIATION
## 3: ATP BIOSYNTHETIC PROCESS
## 4: PROTON MOTIVE FORCE DRIVEN ATP SYNTHESIS
## 5: ELECTRON TRANSPORT CHAIN
## 6: ATP SYNTHESIS COUPLED ELECTRON TRANSPORT
## gs_description
## 1: Any process that mediates the transfer of information from one cell to another. This process includes signal transduction in the receiving cell and, where applicable, release of a ligand and any processes that actively facilitate its transport and presentation to the receiving cell. Examples include signaling via soluble ligands, via cell adhesion molecules and via gap junctions. [GOC:dos, GOC:mah]
## 2: Any process that activates or increases the frequency, rate or extent of neuron differentiation. [GOC:go_curators]
## 3: The chemical reactions and pathways resulting in the formation of ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator. [GOC:go_curators, ISBN:0198506732]
## 4: The transport of protons across a membrane to generate an electrochemical gradient (proton-motive force) that powers ATP synthesis. [ISBN:0716731363]
## 5: A process in which a series of electron carriers operate together to transfer electrons from donors to any of several different terminal electron acceptors. [GOC:mtg_electron_transport]
## 6: The transfer of electrons through a series of electron donors and acceptors, generating energy that is ultimately used for synthesis of ATP. [ISBN:0716731363]if(!require(devtools)) install.packages("devtools")
devtools::install_github("sinhrks/ggfortify")data <- read.csv("../data/GSE271272_rawcounts.csv", row.names = 1)
head(data)## M1_ctrl_Epcam M2_ctrl_Epcam M3_ctrl_Epcam M4_ctrl_Epcam
## 0610009B22Rik 266 41 79 46
## 0610010K14Rik 21 3 3 1
## 0610012G03Rik 276 41 93 74
## 0610030E20Rik 416 88 132 84
## 0610038B21Rik 16 1 4 9
## 0610040J01Rik 765 144 236 120
## M1_EVs_Epcam M2_EVs_Epcam M3_EVs_Epcam M4_EVs_Epcam M1_SFs_Epcam
## 0610009B22Rik 100 59 171 277 198
## 0610010K14Rik 7 2 17 24 19
## 0610012G03Rik 96 113 273 304 254
## 0610030E20Rik 163 106 372 494 443
## 0610038B21Rik 3 1 6 6 12
## 0610040J01Rik 221 170 584 661 692
## M2_SFs_Epcam M3_SFs_Epcam M4_SFs_Epcam M1_OGN_Epcam M2_OGN_Epcam
## 0610009B22Rik 14 84 73 66 78
## 0610010K14Rik 2 3 5 6 5
## 0610012G03Rik 30 89 118 118 94
## 0610030E20Rik 36 188 178 161 129
## 0610038B21Rik 1 4 7 2 4
## 0610040J01Rik 75 222 186 250 230
## M3_OGN_Epcam M4_OGN_Epcam M1_ctrl_CCL M2_ctrl_CCL M3_ctrl_CCL
## 0610009B22Rik 231 40 99 55 136
## 0610010K14Rik 14 2 11 8 16
## 0610012G03Rik 246 59 99 97 178
## 0610030E20Rik 377 84 372 224 364
## 0610038B21Rik 8 1 14 9 9
## 0610040J01Rik 676 192 33 19 34
## M4_ctrl_CCL M1_EVs_CCL M2_EVs_CCL M3_EVs_CCL M4_EVs_CCL
## 0610009B22Rik 111 39 78 94 84
## 0610010K14Rik 12 5 2 5 2
## 0610012G03Rik 135 64 89 125 129
## 0610030E20Rik 401 65 91 287 99
## 0610038B21Rik 16 2 1 3 8
## 0610040J01Rik 24 8 11 18 18
## M1_SFs_CCL M2_SFs_CCL M3_SFs_CCL M4_SFs_CCL M1_OGN_CCL M2_OGN_CCL
## 0610009B22Rik 72 57 67 78 48 135
## 0610010K14Rik 6 3 4 7 6 22
## 0610012G03Rik 114 87 107 111 75 165
## 0610030E20Rik 149 146 213 233 134 371
## 0610038B21Rik 13 3 11 7 8 15
## 0610040J01Rik 23 10 17 12 37 36
## M3_OGN_CCL M4_OGN_CCL
## 0610009B22Rik 163 65
## 0610010K14Rik 16 5
## 0610012G03Rik 268 114
## 0610030E20Rik 583 301
## 0610038B21Rik 21 4
## 0610040J01Rik 129 24t_data = t(data)log_data <- log2(t_data +1)
data_scaled <- scale(log_data)pca_result <- prcomp(data_scaled, scale. = T)# Splits sample naam in onderdelen
metadata <- data.frame(
sample = rownames(t_data),
groep = sapply(strsplit(rownames(t_data), "_"), `[`, 2),
celtype = sapply(strsplit(rownames(t_data), "_"), `[`, 3)
)
rownames(metadata) <- metadata$sample
metadata$groep_celtype <- paste(metadata$groep, metadata$celtype, sep = "_")autoplot(pca_result,
data = metadata,
colour= 'groep',
x=1,
y=2
)+
ggtitle("celtype")+
theme_minimal()
# Pathway analuse Nu ga ik bezig met de path way analyse met de deseq2
die ik van Mirte heb gekregen
rna_seq_data <- read.csv(
"../data/GSE271272_rawcounts.csv")
head(rna_seq_data)## Sample M1_ctrl_Epcam M2_ctrl_Epcam M3_ctrl_Epcam M4_ctrl_Epcam
## 1 0610009B22Rik 266 41 79 46
## 2 0610010K14Rik 21 3 3 1
## 3 0610012G03Rik 276 41 93 74
## 4 0610030E20Rik 416 88 132 84
## 5 0610038B21Rik 16 1 4 9
## 6 0610040J01Rik 765 144 236 120
## M1_EVs_Epcam M2_EVs_Epcam M3_EVs_Epcam M4_EVs_Epcam M1_SFs_Epcam M2_SFs_Epcam
## 1 100 59 171 277 198 14
## 2 7 2 17 24 19 2
## 3 96 113 273 304 254 30
## 4 163 106 372 494 443 36
## 5 3 1 6 6 12 1
## 6 221 170 584 661 692 75
## M3_SFs_Epcam M4_SFs_Epcam M1_OGN_Epcam M2_OGN_Epcam M3_OGN_Epcam M4_OGN_Epcam
## 1 84 73 66 78 231 40
## 2 3 5 6 5 14 2
## 3 89 118 118 94 246 59
## 4 188 178 161 129 377 84
## 5 4 7 2 4 8 1
## 6 222 186 250 230 676 192
## M1_ctrl_CCL M2_ctrl_CCL M3_ctrl_CCL M4_ctrl_CCL M1_EVs_CCL M2_EVs_CCL
## 1 99 55 136 111 39 78
## 2 11 8 16 12 5 2
## 3 99 97 178 135 64 89
## 4 372 224 364 401 65 91
## 5 14 9 9 16 2 1
## 6 33 19 34 24 8 11
## M3_EVs_CCL M4_EVs_CCL M1_SFs_CCL M2_SFs_CCL M3_SFs_CCL M4_SFs_CCL M1_OGN_CCL
## 1 94 84 72 57 67 78 48
## 2 5 2 6 3 4 7 6
## 3 125 129 114 87 107 111 75
## 4 287 99 149 146 213 233 134
## 5 3 8 13 3 11 7 8
## 6 18 18 23 10 17 12 37
## M2_OGN_CCL M3_OGN_CCL M4_OGN_CCL
## 1 135 163 65
## 2 22 16 5
## 3 165 268 114
## 4 371 583 301
## 5 15 21 4
## 6 36 129 24cts <- data.frame(rna_seq_data[,-1])
colnames(cts) <- colnames(rna_seq_data)[-1]
row.names(cts) <- rna_seq_data$Sample
cts <- as.matrix(cts)sample_names <- c("M1_ctrl_Epcam", "M2_ctrl_Epcam", "M3_ctrl_Epcam", "M4_ctrl_Epcam",
"M1_EVs_Epcam", "M2_EVs_Epcam", "M3_EVs_Epcam", "M4_EVs_Epcam",
"M1_SFs_Epcam", "M2_SFs_Epcam", "M3_SFs_Epcam", "M4_SFs_Epcam",
"M1_OGN_Epcam", "M2_OGN_Epcam", "M3_OGN_Epcam", "M4_OGN_Epcam",
"M1_ctrl_CCL", "M2_ctrl_CCL", "M3_ctrl_CCL", "M4_ctrl_CCL",
"M1_EVs_CCL", "M2_EVs_CCL", "M3_EVs_CCL", "M4_EVs_CCL",
"M1_SFs_CCL", "M2_SFs_CCL", "M3_SFs_CCL", "M4_SFs_CCL",
"M1_OGN_CCL", "M2_OGN_CCL", "M3_OGN_CCL", "M4_OGN_CCL")
group <- rep(c("control", "extracellular_vesicles", "soluble_factors", "osteoglycin"), each = 4, times = 2)
type <- rep(c("epcam", "ccl"), each = 16)
metadata <- data.frame(group = factor(group), type = type)
rownames(metadata) <- sample_names
head(metadata)## group type
## M1_ctrl_Epcam control epcam
## M2_ctrl_Epcam control epcam
## M3_ctrl_Epcam control epcam
## M4_ctrl_Epcam control epcam
## M1_EVs_Epcam extracellular_vesicles epcam
## M2_EVs_Epcam extracellular_vesicles epcamdds <- DESeqDataSetFromMatrix(countData = cts,
colData = metadata,
design = ~ group)dds <- DESeq(dds)
control_vs_extracellular <- results(dds, contrast = c("group", "control", "extracellular_vesicles"))
control_vs_soluble <- results(dds, contrast = c("group", "control", "soluble_factors"))
extracellular_vs_soluble <- results(dds, contrast = c("group", "extracellular_vesicles", "soluble_factors"))Na dat alles kan ik verder.
gene_ranks_crl_extra <- control_vs_extracellular$stat
names(gene_ranks_crl_extra) <- rownames(control_vs_extracellular)
gene_ranks_crl_extra <- sort(gene_ranks_crl_extra, decreasing = T)gene_ranks_crl_sol <- control_vs_soluble$stat
names(gene_ranks_crl_sol) <- rownames(control_vs_soluble)
gene_ranks_crl_sol <- sort(gene_ranks_crl_sol, decreasing = T)gene_ranks_extra_sol <- extracellular_vs_soluble$stat
names(gene_ranks_extra_sol) <- rownames(extracellular_vs_soluble)
gene_ranks_extra_sol <- sort(gene_ranks_extra_sol, decreasing = T)Mens
df_genes <- data.frame(
symbol = names(gene_ranks_crl_extra),
stat = gene_ranks_crl_extra
)
df_mapped <- df_genes %>%
left_join(
AnnotationDbi::select(org.Mm.eg.db,
keys = df_genes$symbol,
columns = c("ENTREZID", "SYMBOL"),
keytype = "SYMBOL"),
by = c("symbol" = "SYMBOL")
) %>%
filter(!is.na(ENTREZID))
geneList_1 <- df_mapped$stat
names(geneList_1) <- df_mapped$ENTREZIDgsea_kegg <- gseKEGG(
geneList = geneList_1,
organism = "mmu",
pvalueCutoff = 0.05
)head(gsea_kegg)## ID Description
## mmu03010 mmu03010 Ribosome - Mus musculus (house mouse)
## mmu00190 mmu00190 Oxidative phosphorylation - Mus musculus (house mouse)
## mmu05012 mmu05012 Parkinson disease - Mus musculus (house mouse)
## mmu05020 mmu05020 Prion disease - Mus musculus (house mouse)
## mmu05171 mmu05171 Coronavirus disease - COVID-19 - Mus musculus (house mouse)
## mmu05016 mmu05016 Huntington disease - Mus musculus (house mouse)
## setSize enrichmentScore NES pvalue p.adjust qvalue rank
## mmu03010 130 -0.7221528 -2.977033 1e-10 3.31e-09 2.652632e-09 2643
## mmu00190 104 -0.6814410 -2.728012 1e-10 3.31e-09 2.652632e-09 2288
## mmu05012 209 -0.5799946 -2.532413 1e-10 3.31e-09 2.652632e-09 2535
## mmu05020 197 -0.5497592 -2.392824 1e-10 3.31e-09 2.652632e-09 2535
## mmu05171 165 -0.5415693 -2.303028 1e-10 3.31e-09 2.652632e-09 2643
## mmu05016 237 -0.5165751 -2.286785 1e-10 3.31e-09 2.652632e-09 2596
## leading_edge
## mmu03010 tags=78%, list=23%, signal=61%
## mmu00190 tags=63%, list=20%, signal=52%
## mmu05012 tags=55%, list=22%, signal=44%
## mmu05020 tags=52%, list=22%, signal=41%
## mmu05171 tags=48%, list=23%, signal=38%
## mmu05016 tags=50%, list=22%, signal=40%
## core_enrichment
## mmu03010 75617/20116/67671/19934/121022/68436/67248/64659/20068/54127/20055/94062/27367/19935/66242/27398/100502825/66481/19982/94063/16898/56282/19943/110954/67945/66163/225215/20005/66258/107732/68735/67941/22186/67994/56040/65019/67681/24030/56284/19941/353242/19951/66489/19933/14109/270106/20115/67281/94064/16785/20088/100503670/67025/20104/27370/66419/67707/67186/66475/66292/267019/67097/67115/68565/19921/66480/78294/68028/20102/20085/20084/68463/54217/26451/27176/19989/27397/19899/19981/20044/68537/268449/66845/20054/76846/20042/27050/19896/67891/67427/68052/68611/76808/20103/11837/94065/269261/22121/26961/20091/19988
## mmu00190 11946/66377/66495/54405/140494/28080/68202/66152/11974/66576/11950/74776/11972/66144/67126/17993/67530/27425/22272/12867/108664/11973/69802/72900/57423/11984/11949/67680/66108/78330/69875/11947/12864/17991/22273/225887/66290/66142/11951/73834/66916/66694/230075/66416/228033/67130/67264/68342/75406/68349/20463/12861/110323/66052/12858/66046/71679/104130/66218/68375/68197/67184/66043/17995/12857/226646
## mmu05012 22154/16594/26446/74764/66445/57320/26443/11946/16573/66377/22123/232943/66495/54405/14678/28080/68202/66152/19175/106947/66576/22142/19184/12314/56791/11950/16593/67951/67126/17993/19182/69806/67530/19166/67151/22186/22272/12867/68943/23997/72900/11911/50868/17463/30791/26441/11949/67680/22334/22187/59029/66108/26442/22190/23996/78330/69875/11947/12864/17991/22223/22273/14683/225887/19170/66142/66413/11951/19181/67089/66916/66694/22335/230075/66416/228033/67130/78294/67264/22333/68342/64704/75406/19172/68349/20463/12861/56436/12028/110323/20422/66052/12048/26444/12858/19185/14828/66046/56551/71679/104130/13163/11739/66218/68375/68197/67184/66043/17995/22213/12857/57296/12315/226646
## mmu05020 22154/16594/26446/74764/66445/26443/11946/16573/66377/22123/232943/66495/54405/20867/28080/68202/66152/13001/19353/19175/66576/22142/19184/11950/16593/67951/67126/17993/19182/67530/19166/67151/22272/12867/23997/72900/11911/17463/26441/12015/11949/67680/22334/59029/66108/26442/23996/78330/69875/11947/12864/17991/22273/225887/19170/66142/66413/11951/19181/26427/67089/66916/66694/12913/22335/230075/66416/228033/67130/67264/22333/68342/75406/19055/19172/68349/20463/12861/56436/12028/110323/20422/66052/26444/12858/19185/14828/66046/71679/104130/11739/66218/68375/68197/67184/66043/17995/12857/12915/57296/226646/13057
## mmu05171 75617/20116/67671/19934/20846/68436/67248/20296/20068/54127/20055/27367/24088/100038882/18035/100502825/66481/19982/16898/19943/110954/67945/225215/20005/67941/22186/56040/65019/19941/19951/66489/19933/14109/270106/54131/20115/67281/16785/20088/100503670/67025/20104/27370/67186/66475/267019/67097/67115/19921/66480/78294/68028/20102/20085/20084/54217/26451/27176/19989/19899/19981/20044/268449/20054/76846/20042/27050/18036/19896/67891/67427/68052/76808/20103/11837/269261/22121/26961/20091/19988
## mmu05016 19167/12064/22154/69590/16594/26446/74764/66445/20907/26443/11946/16573/66377/22123/232943/66495/69654/54405/269881/28080/68202/66152/19175/66576/12370/22142/66491/19184/11950/641340/22428/16593/67951/67126/17993/19182/22030/20656/67530/19166/67151/21780/22272/12867/15182/23997/72900/17463/26441/11949/67680/22334/59029/66108/26442/23996/78330/69875/11947/12864/17991/22273/67710/226977/225887/66420/20021/19170/66142/66413/11951/19181/26427/67089/69833/66916/66694/12913/22335/230075/66416/69920/228033/53598/67130/67264/22333/68342/75406/14775/19172/68349/20463/12861/56436/12028/110323/20422/66052/26444/12858/19185/66046/12757/71679/104130/56444/11739/66218/68375/68197/69241/232910/67184/66043/17995/12857/57296/226646dotplot(gsea_kegg, showCategory=5, split=".sign") + facet_grid(.~.sign) + theme(axis.text.y = element_text(size = 9))+ labs(title = 'control vs extracellular')
ridgeplot(gsea_kegg, fill = "p.adjust")+ theme_minimal() + labs(title = 'control vs extracellular')## Picking joint bandwidth of 0.301
Conclusie – Pathwayactivatie in EV-conditie
Aan de hand van de GSEA-analyse (dotplot en ridgeplot) van de vergelijking tussen control en extracellular vesicle (EV) condities, zijn meerdere pathways geïdentificeerd die significant geactiveerd zijn in de EV-conditie. Met name de volgende vier KEGG-pathways vielen op door hun lage p-waarden (p.adjust), hoge genratio’s en duidelijke activatie:
Ribosome – Mus musculus
→ Wijst op verhoogde eiwitsyntheseactiviteit, mogelijk door translatie van mRNA’s afgeleverd door EV’s.
Oxidative phosphorylation – Mus musculus
→ Toename in mitochondriale activiteit en energieproductie, wat zou kunnen duiden op verhoogde metabole behoefte in EV-geactiveerde cellen.
Inositol phosphate metabolism – Mus musculus
→ Betrokken bij intracellulaire signaaloverdracht; activatie kan gerelateerd zijn aan communicatieprocessen tussen cellen via EV’s.
Phosphatidylinositol signaling system – Mus musculus
→ Belangrijk voor celoverleving, groei en signaaltransductie; activatie suggereert mogelijke regulatie van celrespons op EV-inhoud.De combinatie van deze vier pathways suggereert dat EV’s in staat zijn om translationele, metabole en signaaltransductiemechanismen te activeren in ontvangende cellen. Dit wijst op een actieve rol van EV’s in het moduleren van cellulaire functies.
Om deze bevindingen verder te visualiseren en te valideren, worden deze pathways geselecteerd voor visualisatie met pathview op basis van genexpressieverschillen.
terms <- c(
"Ribosome - Mus musculus (house mouse)",
"Oxidative phosphorylation - Mus musculus (house mouse)",
"Inositol phosphate metabolism - Mus musculus (house mouse)",
"Phosphatidylinositol signaling system - Mus musculus (house mouse)"
)
gsea_kegg@result[gsea_kegg@result$Description %in% terms, ]## ID
## mmu03010 mmu03010
## mmu00190 mmu00190
## mmu04070 mmu04070
## mmu00562 mmu00562
## Description
## mmu03010 Ribosome - Mus musculus (house mouse)
## mmu00190 Oxidative phosphorylation - Mus musculus (house mouse)
## mmu04070 Phosphatidylinositol signaling system - Mus musculus (house mouse)
## mmu00562 Inositol phosphate metabolism - Mus musculus (house mouse)
## setSize enrichmentScore NES pvalue p.adjust
## mmu03010 130 -0.7221528 -2.977033 1.000000e-10 3.310000e-09
## mmu00190 104 -0.6814410 -2.728012 1.000000e-10 3.310000e-09
## mmu04070 80 0.4561205 1.975548 3.214553e-05 5.911206e-04
## mmu00562 58 0.4499627 1.828163 5.234797e-04 6.664299e-03
## qvalue rank leading_edge
## mmu03010 2.652632e-09 2643 tags=78%, list=23%, signal=61%
## mmu00190 2.652632e-09 2288 tags=63%, list=20%, signal=52%
## mmu04070 4.737237e-04 3015 tags=52%, list=26%, signal=39%
## mmu00562 5.340764e-03 3680 tags=62%, list=31%, signal=43%
## core_enrichment
## mmu03010 75617/20116/67671/19934/121022/68436/67248/64659/20068/54127/20055/94062/27367/19935/66242/27398/100502825/66481/19982/94063/16898/56282/19943/110954/67945/66163/225215/20005/66258/107732/68735/67941/22186/67994/56040/65019/67681/24030/56284/19941/353242/19951/66489/19933/14109/270106/20115/67281/94064/16785/20088/100503670/67025/20104/27370/66419/67707/67186/66475/66292/267019/67097/67115/68565/19921/66480/78294/68028/20102/20085/20084/68463/54217/26451/27176/19989/27397/19899/19981/20044/68537/268449/66845/20054/76846/20042/27050/19896/67891/67427/68052/68611/76808/20103/11837/94065/269261/22121/26961/20091/19988
## mmu00190 11946/66377/66495/54405/140494/28080/68202/66152/11974/66576/11950/74776/11972/66144/67126/17993/67530/27425/22272/12867/108664/11973/69802/72900/57423/11984/11949/67680/66108/78330/69875/11947/12864/17991/22273/225887/66290/66142/11951/73834/66916/66694/230075/66416/228033/67130/67264/68342/75406/68349/20463/12861/110323/66052/12858/66046/71679/104130/66218/68375/68197/67184/66043/17995/12857/226646
## mmu04070 53332/242291/107650/18750/320404/16440/380921/18798/224020/110524/16438/320634/18704/18711/104015/17772/69718/18718/219135/83493/327655/18708/227399/18752/170749/269180/97287/110911/74302/16330/108083/101490/18717/56077/19211/18795/18803/72519/16439/18797/13139/77116
## mmu00562 53332/103199/107650/320404/18798/224020/320634/104776/18704/18711/104015/17772/69718/18718/219135/83493/170749/269180/97287/19062/74302/16330/108083/101490/18717/19211/18795/18803/18797/77116/18706/20975/117150/67073/16332/114663setwd("imgs_trans/control_vs_extracellular")
# KEGG: Oxidative phosphorylation (mmu00190)
pathview(
gene.data = geneList_1,
pathway.id = "mmu00190",
species = "mmu",
kegg.native = TRUE,
out.suffix = "oxphos"
)
# KEGG: Purine metabolism (mmu00562)
pathview(
gene.data = geneList_1,
pathway.id = "mmu00562",
species = "mmu",
kegg.native = TRUE,
out.suffix = "metabolism"
)
# KEGG: Ribosome (mmu03010)
pathview(
gene.data = geneList_1,
pathway.id = "mmu03010",
species = "mmu",
kegg.native = TRUE,
out.suffix = "translation"
)
# KEGG: signaling (mmu03010)
pathview(
gene.data = geneList_1,
pathway.id = "mmu04070",
species = "mmu",
kegg.native = TRUE,
out.suffix = "signaling"
)
1. Oxidative Phosphorylation
(mmu00190) De meeste genen in dit mitochondriale energieproductiepad
zijn sterk opgereguleerd (groen), vooral binnen complex I
(NADH-dehydrogenase), complex III (cytochroom bc1), complex IV
(cytochroom c oxidase) en complex V (ATP-synthase). Dit duidt op
verhoogde oxidatieve fosforylatie-activiteit, wat wijst op verhoogde
energiebehoefte of -productie als reactie op EV-behandeling
Purine metabolism
- Inositol Phosphate Metabolism (mmu00562) Deze afbeelding toont een gemengd beeld van genregulatie: meerdere enzymen zijn opgereguleerd (zoals 2.7.8.11, 3.1.3.62), terwijl anderen sterk onderdrukt zijn (zoals 3.1.3.56, 3.1.3.66). Dit suggereert actieve herprogrammering van signaaltransductie via inositolfosfaatroutes, mogelijk als reactie op EV-afgeleide signalen.
Ribosome
- Ribosome (mmu03010) De visualisatie laat een alomtegenwoordige opregulatie zien van genen die coderen voor ribosomale eiwitten in zowel de kleine als grote subeenheid (vrijwel alles is groen). Dit wijst op een duidelijke stimulatie van eiwitsynthese, waarschijnlijk om cellen voor te bereiden op verhoogde translatie-activiteit door de invloed van EV’s.
Oxidative phosphorylation
- Phosphatidylinositol Signaling System (mmu04070) Deze pathway toont een complex regulatiepatroon: enkele sleutelenzymen zoals 2.7.8.11 en CALM zijn geactiveerd, terwijl veel PI3K-gerelateerde enzymen (zoals PIK3C2, PLC, PTEN) onderdrukt zijn. Dit suggereert gerichte modulatie van intracellulaire signaalroutes, met een mogelijke verschuiving in balans tussen activatie en remming van het PI3K-Akt pad. EV’s kunnen zo fijnmazig signaaltransductie beïnvloeden.
df_genes_2 <- data.frame(
symbol = names(gene_ranks_crl_sol),
stat = gene_ranks_crl_sol
)
df_mapped_2 <- df_genes_2 %>%
left_join(
AnnotationDbi::select(org.Mm.eg.db,
keys = df_genes_2$symbol,
columns = c("ENTREZID", "SYMBOL"),
keytype = "SYMBOL"),
by = c("symbol" = "SYMBOL")
) %>%
filter(!is.na(ENTREZID))
sol_gen <- df_mapped_2$stat
names(sol_gen) <- df_mapped_2$ENTREZIDgsea_kegg_2 <- gseKEGG(
geneList = sol_gen,
organism = "mmu",
pvalueCutoff = 0.05,
seed= 1
)## preparing geneSet collections...## GSEA analysis...## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.## leading edge analysis...## done...head(gsea_kegg_2)## ID Description setSize
## mmu04657 mmu04657 IL-17 signaling pathway - Mus musculus (house mouse) 61
## enrichmentScore NES pvalue p.adjust qvalue rank
## mmu04657 -0.5257455 -1.928895 0.0001482211 0.04906118 0.0449344 2031
## leading_edge
## mmu04657 tags=38%, list=17%, signal=31%
## core_enrichment
## mmu04657 20306/26419/17386/20296/12370/71609/59008/68652/14082/22030/22031/56480/12675/18033/14825/19225/103213/239114/56489/20297/330122/20311/16819dotplot(gsea_kegg_2, showCategory=5, split=".sign") + facet_grid(.~.sign) + theme(axis.text.y = element_text(size = 9))+ labs(title = 'Control vs soluble')
ridgeplot(gsea_kegg_2, showCategory = 10, fill = "p.adjust")+ theme_minimal()+ labs(title = 'Control vs soluble')## Picking joint bandwidth of 0.145
Conclusie – Pathwaysuppressie in SF-conditie
Aan de hand van de GSEA-analyse (dotplot en ridgeplot) van de vergelijking tussen control en soluble factors (SF’s), zijn twee KEGG-pathways geïdentificeerd die significant onderdrukt zijn in de SF-conditie. Deze pathways vielen op door hun lage p-waarden (p.adjust), consistente negatieve genexpressie en duidelijke suppressie:
Chemokine signaling pathway – Mus musculus
→ Onderdrukking van genen die betrokken zijn bij immuuncelactivatie en -migratie, wijzend op een remming van ontstekingsprocessen.
IL-17 signaling pathway – Mus musculus
→ IL-17 is cruciaal voor pro-inflammatoire signalering; de sterke onderdrukking van deze route suggereert dat SF’s ontstekingsreacties actief temperen.
De gelijktijdige onderdrukking van deze twee immuun-gerelateerde pathways wijst erop dat SF’s mogelijk een immunosuppressieve werking uitoefenen op ontvangende cellen. Dit zou kunnen bijdragen aan een verminderde immuunactivatie of ontstekingsremming.Om deze bevindingen verder te verkennen en visueel inzicht te krijgen in welke genen binnen deze pathways onderdrukt zijn, worden deze twee routes geselecteerd voor visualisatie met pathview op basis van differentiële genexpressie.
terms <- c(
"Chemokine signaling pathway - Mus musculus (house mouse)",
"IL-17 signaling pathway - Mus musculus (house mouse)"
)
gsea_kegg_2@result[gsea_kegg_2@result$Description %in% terms, ]## ID Description setSize
## mmu04657 mmu04657 IL-17 signaling pathway - Mus musculus (house mouse) 61
## enrichmentScore NES pvalue p.adjust qvalue rank
## mmu04657 -0.5257455 -1.928895 0.0001482211 0.04906118 0.0449344 2031
## leading_edge
## mmu04657 tags=38%, list=17%, signal=31%
## core_enrichment
## mmu04657 20306/26419/17386/20296/12370/71609/59008/68652/14082/22030/22031/56480/12675/18033/14825/19225/103213/239114/56489/20297/330122/20311/16819setwd("imgs_trans/control_vs_soluble")
#IL-17 signaling pathway
pathview(
gene.data = gsea_kegg_2,
pathway.id = "mmu04657",
species = "mmu",
out.suffix = "IL-17"
)
#Chemokine signaling pathway
pathview(
gene.data = gsea_kegg_2,
pathway.id = "mmu04062",
species = "mmu",
out.suffix = "Chemokine"
)
IL-17 signaling pathway
Deze pathway laat zien hoe binding van verschillende IL-17-cytokines leidt tot activatie van downstream signaalroutes, waaronder NF-κB, MAPK en C/EBP. Hierdoor ontstaat een sterke transcriptie van ontstekingsbevorderende genen zoals IL-6, TNF-α en diverse chemokinen. Daarnaast wordt ook de expressie van antimicrobiële eiwitten en matrixmetallo-proteïnasen gestimuleerd, wat duidt op betrokkenheid bij afweer en weefselremodellering. De pathway is dus sterk gericht op het reguleren van ontsteking, immuunactivatie en celrespons op infectie of schade.
Chemokine signaling pathway
In deze pathway activeert chemokine-receptorbinding meerdere intracellulaire routes zoals PI3K/Akt, MAPK en Rho-GTPase-activiteiten. Deze signalen leiden tot celoverleving, cytokineproductie, actineherstructurering en migratie van immuuncellen. Verder speelt deze pathway een rol bij de regulatie van leukocytenmigratie via het endotheel, ROS-productie en apoptose. De pathway ondersteunt daarmee een brede afstemming van immuuncelgedrag tijdens ontstekingsreacties en immuunresponsen.
df_genes_3 <- data.frame(
symbol = names(gene_ranks_extra_sol),
stat = gene_ranks_extra_sol
)
df_mapped_3 <- df_genes_3 %>%
left_join(
AnnotationDbi::select(org.Mm.eg.db,
keys = df_genes_3$symbol,
columns = c("ENTREZID", "SYMBOL"),
keytype = "SYMBOL"),
by = c("symbol" = "SYMBOL")
) %>%
filter(!is.na(ENTREZID))## 'select()' returned 1:many mapping between keys and columnsgeneList_3 <- df_mapped_3$stat
names(geneList_3) <- df_mapped_3$ENTREZIDgsea_kegg_3 <- gseKEGG(
geneList = geneList_3,
organism = "mmu",
pvalueCutoff = 0.05
)dotplot(gsea_kegg_3, showCategory=5, split=".sign") + facet_grid(.~.sign) + theme(axis.text.y = element_text(size = 9)) + labs(title = 'Extracellular vs soluble')
ridgeplot(gsea_kegg_3, showCategory = 10, fill = "p.adjust")+ theme_minimal()+ theme(axis.text.y = element_text(size = 9))+ labs(title = 'Extracellular vs soluble')## Picking joint bandwidth of 0.255
Conclusie – Pathwayactivatie in EV t.o.v. SF-conditie
Aan de hand van de GSEA-analyse (dotplot en ridgeplot) van de vergelijking tussen extracellular vesicles (EV’s) en soluble factors (SF’s), zijn meerdere pathways geïdentificeerd die significant geactiveerd zijn in de EV-conditie. De volgende vier KEGG-pathways vallen op door hun lage p.adjust-waarden, hoge genratio’s en uitgesproken activatie:
Ribosome – Mus musculus
→ Sterke activatie van ribosomale genen wijst op verhoogde eiwitsynthesecapaciteit in EV-behandelde cellen.
Oxidative phosphorylation – Mus musculus
→ Verhoogde mitochondriale activiteit en energieproductie; suggereert een toegenomen metabole vraag onder invloed van EV’s.
Inositol phosphate metabolism – Mus musculus
→ Betrokken bij signaaltransductie; activatie impliceert versterkte intracellulaire communicatieprocessen.
Phosphatidylinositol signaling system – Mus musculus
→ Belangrijk voor celgroei, overleving en PI3K/Akt-pathway-regulatie; activatie suggereert versterking van overlevings- en groeisignalen.De gelijktijdige activatie van deze pathways laat zien dat EV’s een krachtiger effect uitoefenen op fundamentele cellulaire processen dan SF’s. Ze stimuleren translatie, energieproductie en signaalroutes die cruciaal zijn voor celactivatie en functie.
Om deze bevindingen visueel te onderbouwen worden deze vier pathways geselecteerd voor Pathview-visualisatie op basis van differentiële genexpressie tussen EV- en SF-condities.
terms_best <- c(
"Ribosome - Mus musculus (house mouse)",
"Oxidative phosphorylation - Mus musculus (house mouse)",
"Inositol phosphate metabolism - Mus musculus (house mouse)",
"Phosphatidylinositol signaling system - Mus musculus (house mouse)"
)
gsea_kegg_3@result[gsea_kegg_3@result$Description %in% terms_best, ]## ID
## mmu03010 mmu03010
## mmu00190 mmu00190
## mmu04070 mmu04070
## mmu00562 mmu00562
## Description
## mmu03010 Ribosome - Mus musculus (house mouse)
## mmu00190 Oxidative phosphorylation - Mus musculus (house mouse)
## mmu04070 Phosphatidylinositol signaling system - Mus musculus (house mouse)
## mmu00562 Inositol phosphate metabolism - Mus musculus (house mouse)
## setSize enrichmentScore NES pvalue p.adjust
## mmu03010 130 0.7411469 3.081845 1.000000e-10 2.546154e-09
## mmu00190 104 0.6982908 2.820457 1.000000e-10 2.546154e-09
## mmu04070 80 -0.4819740 -2.033392 7.883228e-06 1.373341e-04
## mmu00562 58 -0.4790070 -1.905681 3.237648e-04 4.871188e-03
## qvalue rank leading_edge
## mmu03010 1.862348e-09 2105 tags=75%, list=18%, signal=63%
## mmu00190 1.862348e-09 1833 tags=61%, list=16%, signal=52%
## mmu04070 1.004511e-04 3354 tags=56%, list=29%, signal=40%
## mmu00562 3.562962e-03 3293 tags=59%, list=28%, signal=42%
## core_enrichment
## mmu03010 19988/20091/26961/20103/27397/67427/68052/27050/19981/67891/20054/19989/20042/269261/94065/76846/11837/19896/76808/22121/68537/20044/268449/19899/68463/68611/27398/78294/68028/94064/66419/20085/67707/20102/27176/26451/68565/66480/20104/66845/94063/66258/16785/19921/20088/67025/54217/67941/67115/121022/19933/66475/20084/66292/267019/67281/20115/66489/19951/67186/19941/107732/67945/100503670/56282/67681/118451/68735/56040/225215/27370/65019/20055/110954/94062/67097/270106/20005/353242/22186/14109/67994/19935/20068/19943/20116/24030/56284/67671/16898/67248/66481/68436/19982/94066/75617/66223/27367
## mmu00190 17995/226646/66052/66043/67184/75406/68349/66046/110323/12857/68375/71679/104130/66218/68197/66916/12861/20463/68342/11951/73834/66694/11949/12858/22273/12867/225887/67680/67264/67530/12864/67126/27425/57423/17991/228033/69875/108664/11947/66108/66416/66142/66290/66152/66576/230075/72900/74776/22272/11973/68202/17992/67130/66945/11984/17993/11950/11946/66925/54405/11972/66495/114143
## mmu04070 16439/18706/67073/18751/19211/16330/320404/101490/16329/74302/234515/18717/18720/18797/56077/69718/17772/110911/20975/18708/227399/320634/16332/18718/83493/18704/219135/18711/16438/18803/242291/170749/53332/108083/327655/380921/104015/269180/18752/110524/224020/16440/18750/18798/107650
## mmu00562 18706/67073/19211/16330/320404/101490/16329/103199/74302/234515/18717/18720/18797/69718/17772/20975/104776/320634/16332/18718/83493/18704/219135/18711/18803/170749/53332/108083/104015/269180/19062/224020/18798/107650setwd("imgs_trans/extracellular_vs_soluble")
# Ribosome / cytoplasmic translation
pathview(
gene.data = geneList_3,
pathway.id = "mmu03010",
species = "mmu",
out.suffix = "ribosome"
)## 'select()' returned 1:1 mapping between keys and columns## Info: Working in directory /homes/jjonker2/Documents/Omics/omic_epithelial_cell_repair/Logfiles/imgs_trans/extracellular_vs_soluble## Info: Writing image file mmu03010.ribosome.png#Phosphatidylinositol signaling system
pathview(
gene.data = geneList_3,
pathway.id = "mmu04070",
species = "mmu",
out.suffix = "mito_translation"
)## 'select()' returned 1:1 mapping between keys and columns## Info: Working in directory /homes/jjonker2/Documents/Omics/omic_epithelial_cell_repair/Logfiles/imgs_trans/extracellular_vs_soluble## Info: Writing image file mmu04070.mito_translation.png# Oxidative phosphorylation
pathview(
gene.data = geneList_3,
pathway.id = "mmu00190",
species = "mmu",
out.suffix = "Phosphatidylinositol"
)## 'select()' returned 1:1 mapping between keys and columns## Info: Working in directory /homes/jjonker2/Documents/Omics/omic_epithelial_cell_repair/Logfiles/imgs_trans/extracellular_vs_soluble## Info: Writing image file mmu00190.Phosphatidylinositol.png# Inositol phosphate metabolism
pathview(
gene.data = geneList_3,
pathway.id = "mmu00562",
species = "mmu",
out.suffix = "metabolism"
)## 'select()' returned 1:1 mapping between keys and columns## Info: Working in directory /homes/jjonker2/Documents/Omics/omic_epithelial_cell_repair/Logfiles/imgs_trans/extracellular_vs_soluble## Info: Writing image file mmu00562.metabolism.png
Ribosome pathway
In deze pathway is te zien dat vrijwel alle ribosomale eiwitgenen zijn opgereguleerd in de EV-conditie ten opzichte van SF. Zowel de grote als kleine ribosoomsubeenheid laat sterke activatie zien. Dit wijst op een verhoogde eiwitsyntheseactiviteit, mogelijk doordat EV’s translationele processen stimuleren.
Phosphatidylinositol signaling
system
De visualisatie toont brede activatie van genen betrokken bij PI3K/Akt-signalen, PLC-activatie en fosfatidylinositolomzettingen. Dit patroon wijst op versterkte intracellulaire signaaltransductie, celoverleving en groeisignalen die door EV’s op gang kunnen zijn gebracht.
Oxidative phosphorylation
De figuur laat duidelijke activatie zien van mitochondriale genen verspreid over complex I tot en met complex V. Deze upregulatie van oxidatieve fosforylering wijst op verhoogde ATP-productie en een grotere energiebehoefte in cellen die zijn blootgesteld aan EV’s.
Oxidative phosphorylation
Inositol phosphate metabolism
In deze pathway zijn meerdere enzymen actief betrokken bij de afbraak en omzetting van inositolfosfaten zichtbaar geactiveerd. Dit suggereert versterkte signaaloverdracht via second messengers, mogelijk als gevolg van EV-gerelateerde stimulatie van celinterne communicatie.
Muis
df_genes_mens <- data.frame(
symbol = names(gene_ranks_crl_extra),
stat = gene_ranks_crl_extra
)
df_mapped_mens <- df_genes %>%
left_join(
AnnotationDbi::select(org.Hs.eg.db,
keys = df_genes$symbol,
columns = c("ENTREZID", "SYMBOL"),
keytype = "SYMBOL"),
by = c("symbol" = "SYMBOL")
) %>%
filter(!is.na(ENTREZID))
geneList_Mens <- df_mapped_mens$stat
names(geneList_Mens) <- df_mapped_mens$ENTREZIDCoronavirus
term <- c(
"Coronavirus disease - COVID-19 - Mus musculus (house mouse)")
gsea_kegg@result[gsea_kegg@result$Description %in% term, ]## ID Description
## mmu05171 mmu05171 Coronavirus disease - COVID-19 - Mus musculus (house mouse)
## setSize enrichmentScore NES pvalue p.adjust qvalue rank
## mmu05171 165 -0.5415693 -2.303028 1e-10 3.31e-09 2.652632e-09 2643
## leading_edge
## mmu05171 tags=48%, list=23%, signal=38%
## core_enrichment
## mmu05171 75617/20116/67671/19934/20846/68436/67248/20296/20068/54127/20055/27367/24088/100038882/18035/100502825/66481/19982/16898/19943/110954/67945/225215/20005/67941/22186/56040/65019/19941/19951/66489/19933/14109/270106/54131/20115/67281/16785/20088/100503670/67025/20104/27370/67186/66475/267019/67097/67115/19921/66480/78294/68028/20102/20085/20084/54217/26451/27176/19989/19899/19981/20044/268449/20054/76846/20042/27050/18036/19896/67891/67427/68052/76808/20103/11837/269261/22121/26961/20091/19988setwd("imgs_trans/corona")
pathview(
gene.data = geneList_1,
pathway.id = "mmu05171",
species = "mmu",
out.suffix = "Corno_1"
)## 'select()' returned 1:1 mapping between keys and columns## Info: Working in directory /homes/jjonker2/Documents/Omics/omic_epithelial_cell_repair/Logfiles/imgs_trans/corona## Info: Writing image file mmu05171.Corno_1.pngpathview(
gene.data = sol_gen,
pathway.id = "mmu05171",
species = "mmu",
out.suffix = "Corno_2"
)## 'select()' returned 1:1 mapping between keys and columns## Info: Working in directory /homes/jjonker2/Documents/Omics/omic_epithelial_cell_repair/Logfiles/imgs_trans/corona## Info: Writing image file mmu05171.Corno_2.pngpathview(
gene.data = geneList_3,
pathway.id = "mmu05171",
species = "mmu",
out.suffix = "Corno_3"
)## 'select()' returned 1:1 mapping between keys and columns## Info: Working in directory /homes/jjonker2/Documents/Omics/omic_epithelial_cell_repair/Logfiles/imgs_trans/corona## Info: Writing image file mmu05171.Corno_3.png
Corona in sfs vs evs